Cert++

Practice
Certle
Review
Tracks
Checklist
Guides
Upgrade

Home
Agentforce Specialist

Agentforce Specialist

Checklist progress

0/163Learned

Agentforce Specialist

Study Checklist

Platform Administrator
Platform App Builder
Platform Foundations
Platform Developer
Platform Administrator II
Agentforce Sales Consultant
Agentforce Service Consultant
Platform Data Architect
Platform Development Lifecycle and Deployment Architect
Platform Identity and Access Management Architect
Platform Integration Architect
Platform Sharing and Visibility Architect
Heroku Architect
B2C Solution Architect
Experience Cloud Consultant
Agentforce Field Service and Operations Consultant
Agentforce Nonprofit Consultant
Data 360 Consultant
Omnistudio Consultant
CRM Analytics and Einstein Discovery Consultant
Platform User Experience Designer
Platform Strategy Designer
B2C Commerce Developer
JavaScript Developer
Omnistudio Developer
Platform Developer II
Marketing Cloud Engagement Administrator
Marketing Cloud Engagement Specialist
Marketing Cloud Engagement Consultant
Agentforce Sales Foundations
Business Analyst
Marketing Cloud Engagement Developer
Marketing Cloud Engagement Foundations
Agentforce Specialist
Agentforce Life Sciences Consultant
B2B Commerce Administrator AP
B2B Commerce Developer AP
Agentforce Consumer Goods AP
Agentforce Financial Services AP
Agentforce Health AP
Agentforce Manufacturing AP
MuleSoft Integration Foundations
MuleSoft Developer
MuleSoft Developer II
MuleSoft Platform Integration Architect
MuleSoft Platform Architect
Tableau Desktop Foundations
Tableau Data Analyst
Tableau Consultant
Tableau Server Administrator
Tableau Architect

Checklist progress

0/163Learned

When to use Prompt Builder
Prompt Builder versus predictive AI
Prompt Builder versus Flow automation
Prompt Builder versus Agent Builder
Prompt templates as Agentforce actions
Prompt Builder enablement and user assignment
Prompt Template Manager versus Prompt Template User
Type-specific permissions for prompt templates
Running-user data access for prompt templates
Field Generation prompt templates
Flex prompt templates
Sales Email prompt templates
Record Summary prompt templates
CRM merge fields for prompt grounding
Flow versus merge fields for prompt grounding
Apex merge fields versus Flow for prompt grounding
Knowledge RAG grounding in prompt templates
Data 360 retriever grounding in Prompt Builder
Combining merge fields with retrieval grounding
Web grounding for real-time external data
File input grounding in Prompt Builder
Prompt template creation in Prompt Builder
Prompt template preview and revision
Prompt template activation and draft status
Prompt template execution surfaces
Prompt template anatomy and design
Merge fields versus hardcoded values in prompt text
Grounded-response instructions in prompt templates
Context engineering versus prompt engineering
Context clash, confusion, and poisoning
Einstein Trust Layer architecture
Secure data retrieval for prompt grounding
Pattern-based and field-based LLM data masking
Prompt defense system policies
Zero-data retention with third-party LLMs
Toxicity detection on generated responses
Generative AI audit trail and feedback data
Supported LLMs for Prompt Builder and Agentforce
Org-level model enablement and blocking
Prompt template model selection
Agentforce model option and subagent overrides

Purpose of Agentforce Data Library
Data Library source types
Knowledge grounding with a Data Library
Data Library setup versus runtime
Data Library setup considerations
Assigning a Data Library to an agent
Default versus additional Data Libraries
Custom retrievers in a Data Library
Data Library versus advanced RAG setup
Retrieval augmented generation in Data 360
Chunking sources for AI retrieval
Search indexes for chunked Data 360 content
Vector versus hybrid search indexes
Retrievers versus search indexes
Retriever creation and activation for grounding
Ensemble retrievers in Data 360
Knowledge retrieval troubleshooting for agents

Agentforce agent building blocks
Atlas reasoning engine request handling
Subagents versus actions
Action chaining and subagent transitions
Agent Script as the Agentforce Builder language
Agent Script block types
Next-generation authoring of Agent Script
Hybrid reasoning components in Agent Script
Benefits of hybrid reasoning
Agent Script in Canvas View
Agent Script in Script View
Canvas versus Script View
Before and after reasoning in Agent Script
Topic filters for request gating
Action filters for business-rule gating
Input variables versus hardcoded action values
Output variables for action-to-action data
Context and conversation variables
Template expressions in agent instructions
Conditional logic for context-specific prompts
Standard versus custom topics
Topic classification descriptions and routing
Custom topic scope and instructions
Standard versus custom agent actions
Custom action types from Flow, Apex, and prompt templates
Creating custom agent actions
Agent action descriptions and instructions
Topic-level versus action-level instructions
Assigning actions to a topic
User access for standard agent actions
Supported channels for Employee and Service agents
Service Agent connections to digital experiences
Service Agent connections to messaging channels
Service Agent connections to email
Service Agent connections to Agentforce Voice
Omni-Channel routing to a Service Agent
Employee Agent connections to Slack
Employee Agent connections to Lightning Experience and Mobile
Execution identity by agent type
Running-user permissions and sharing for actions
Employee agent access for end users
Credential-based verification and Service run-as
Identity verification in sensitive agent actions
Context variables versus data access control
Employee versus Service agent selection
Agentforce Default versus Employee agent
When to use Agent API versus standard channels
Agent API supported agent types
Agent API versus Models API
Agent API versus custom agent actions
Agent API versus other Agentforce APIs and SDKs
Agent API actions in Flow and Apex

Given a scenario, test an agent using Testing Center

0/8

Agent testing strategy from preparation through retest
Agentforce Builder versus Testing Center testing
Builder Simulate versus Live Test modes
Permissions for Testing Center tests
Testing Center test case criteria
Test data and conversation context for Testing Center
Batch testing and iteration in Testing Center
AI-generated tests in Testing Center

Explain how Testing Center evaluations work

0/6

Testing Center subagent and action evaluation

Learn this concept

Unseen

Testing Center response evaluation

Learn this concept

Unseen

Testing Center results interpretation

Learn this concept

Unseen

Quality metric scorers in Testing Center

Learn this concept

Unseen

Custom Testing Center scorers

Learn this concept

Unseen

Testing Center limits and considerations

Learn this concept

Unseen

Identify the considerations for deploying an agent from sandbox to production.

0/7

Change sets versus Agentforce DX for agent deployment
Dependent components in an agent change set
Target-org prerequisites for a deployed agent
Agent activation after production deployment
Post-deployment channel and connection configuration
Agent versioning for uninterrupted production updates
Data 360 and Data Library considerations for agent promotion

Identify the considerations for deploying a template from sandbox to production

0/7

Prompt template change sets and metadata deployment
Dependent metadata for prompt template deployment
Prompt Builder enablement in the production org
Prompt template activation in the destination org
Prompt template version behavior on deploy
Field generation Lightning page assignment after deploy
Retriever and Knowledge grounding after template deployment

Monitoring in the Agent Development Lifecycle
Agent manager permission sets
Activate and deactivate agents
Agent versioning lifecycle
Agentforce guardrails and trust patterns
AI agent risk identification
Agent guardrail monitoring
Einstein Audit and Feedback setup
Audit trail and toxicity review
Enhanced Event Logs for agent monitoring
Investigating agent sessions and intents
Omni-Channel monitoring for Service agents
Agent Analytics versus Agent Optimization
Agent Analytics metrics dashboards and session insights
Session Tracing Data Model for observability
Intents in Agent Optimization
Utterance analysis for agent conversations
Quality scores and subagent performance trends
Scorers and custom scorers in Agentforce Studio
Context engineering for agent optimization
Legacy Agentforce Analytics versus Agent Analytics

Single-agent versus Multi Agent architecture
Subagents versus a Multi Agent architecture
When to use Model Context Protocol (MCP)
When to use Agent-to-Agent (A2A) protocol

Prepare for the Exam

Play Today's Certle

Study Community

Ask questions and get the latest info from other Agentforce Specialist studiers.

Custom Testing Center scorers

Explainer

Learn More

Practice Question

Keep going

Next conceptTesting Center limits and considerations

Checklist progress

0/163 (0%)

0 of 163 concepts learned

Learned0Learning0Seen0Unseen163

Tip: You can filter concepts by status.

Prepare for the Exam

Play Today's Certle

Study Community

Ask questions and get the latest info from other Agentforce Specialist studiers.

Explainer

Custom Testing Center scorers let you evaluate an agent against criteria you define, such as brand voice, quality expectations, and intended sentiment. They use an LLM-as-judge: one large language model scores the agent's response from a prompt you write. After you save a custom scorer, Testing Center automatically selects it for the test suite, and Run Test applies the chosen scorers to the test cases.

Core information

Default Testing Center scorers always run; custom scorers sit alongside them so you can define metrics tailored to the agent's purpose instead of judging effectiveness with only simple pass or fail checks.

More details and nuances

Custom LLM Judge scorers apply to the new Testing Center in Agentforce Studio (Beta), not the legacy Agentforce Testing Center in Setup.